kobyszcze

5 ms·

1. 1.▲
   Building a statically compiled, PyTorch-like autodifferentiation library in Rust(medium.com)

   3 points by kobyszcze 9y ago|0 comments
2. 2.▲
   Show HN: FizzBuzz using neural networks in Rust(github.com)

   5 points by kobyszcze 9y ago|1 comments
3. 3.▲
   Spotlight: deep learning recommender models in PyTorch(github.com)

   2 points by kobyszcze 9y ago|0 comments
4. 4.▲
   by kobyszcze 11y ago

   You could do a lot of ML without GPU support. Especially if you are dealing with sparse data (like text).
5. 5.▲
   by kobyszcze 11y ago

   I wrote a simple example of calling out into Rust from Python using cffi: https://github.com/maciejkula/python-rustlearn . The gist of it is: it's really easy.
6. 6.▲
   Rust machine learning library(github.com)

   2 points by kobyszcze 11y ago|0 comments
7. 7.▲
   LightFM: a learning-to-rank hybrid recommender package for Python(github.com)

   1 points by kobyszcze 11y ago|0 comments
8. 8.▲
   by kobyszcze 12y ago

   You could use identical priors for each variant, with the mean equal to the empirical mean of your existing system, and the variance controlling how plausible big improvements are. In this case your prior mean could be something as simple a
9. 9.▲
   by kobyszcze 12y ago

   Thanks for the comment. We do look at comparing proportions in two samples (have a look at our calculator: http://developers.lyst.com/bayesian-calculator/ ). The way we do this is by constructing a posterior distributio
10. 10.▲
    by kobyszcze 12y ago

    Thanks for the comment! What we were trying to get at is running repeated experiments when prior probability of an experiment being successful is low---which you correctly point out is also about repeated testing (and has nothing to do with